Flora Amato

Model-driven engineering of cloud components in MetaMORPhOSY

Cloud architecture is established as a standard for services provisioning. Discovering of services is a hard task since several services have to be analysed. The mOSAIC project aims at providing a framework for searching and interfacing services from multiple vendors. Its main component, the cloud agency, provides searching and wrapping features and the management of Services Level Agreements SLAs. In this scenario, validating services requirements is appealing. Usually users are forced to use SLAs pre-offered by providers that need to reconfigure cloud components in order to meet requests. Model-Driven Engineering MDE methodologies allow for requirements verification during all life cycle of systems. They can be exploited in definition of models for cloud components that have to be used and configured in order to fulfil requested service levers. Here, the MetaMORPhOSY methodology and framework are introduced and applied to the verification of SLAs in the mOSAIC platform.

Knowledge Representation and Management for E-Government Documents

In the last few years bureaucratic procedures didn’t show a significant reduction in the volume of paper documents created. In order to reduce the huge amount of space for archiving and preserving documents and to speed up the secarh process, a semantic-based dematerialization process should be performed. In this paper we describe a novel system that manages several kind of bureaucratic documents in the e-gov domain, automatically extracts several interesting information and produces a suitable semantic representation that may be considered as the first step towards a full automated document management system.

Information Extraction from Multimedia Documents for e-Government Applications

Despite the exponential growth of information systems for supporting public administration requirements, we are still far from a complete automatic e-government system. In particular, there exists the need of automatic or semi-automatic procedures for the whole flow of digital documents management, in particular regarding: (1) automatic information extraction from digital documents; (2) semantic interpretation (3) storing; (4) long term preservation and (5) retrieval of the extracted information. In addition, in the last few years the textual information has been enriched with multimedia data, having heterogeneous formats and semantics. In this framework, it’s the author’s opinion that an effective E-Government information system should provide tools and techniques for multimedia information, in order to manage both the multimedia content of a bureaucratic document and the presentation constraints that are usually associated to such document management systems. In this paper, we will describe a novel system that exploits both textual and image processing techniques, in order to automatically infer knowledge from multimedia data, thus simplifying the indexing and retrieval tasks. A prototypal version of the system has been developed and some preliminary experimental results have been carried out, demonstrating the efficacy in real application contexts.

A Modeling Profile for Availability Analysis of Composite Cloud Services

Service Oriented Architectures and service-centric models arose in the last years as a standard de-facto in IT enterprises for offering practically any kind of services to users world-wide. Heterogeneity (of operating systems, programming languages, software and hardware infrastructures etc.) and transparency (for users) are the key issues of the actual trends in developing and providing services. In particular Cloud-based models provide means for realizing and distributing everything-as-a-service, including infrastructures, hardware and software platforms and services. Even if at the moment Service-centric models and technologies are mature in the IT scenario, Composition, analysis and validation of Cloud services is an open research challenge. In this work we describe a methodology based on Multi-Agent Models which allows for description, composition and verification of requirements of Cloud-based services. The methodology uses a modeling profile able to describe services as agents in a multi-agent environment and it is based on Model Driven Engineering (MDE) techniques. The proposed methodology includes a verification process for requirements that exploits formal methods during the whole life cycle of services. An example of the application of the proposed techniques will be reported also.

Automatic Population of Italian Medical Thesauri: A Morphosemantic Approach

In the age of Semantic Web, one of the most valuable challenges is the one connected with the information extraction from raw data. Information must be managed with sophisticated linguistic and computational architectures, which are able to approach the semantic dimension of words and sentences. In this paper we propose a morphosemantic method for the automatic creation and population of medical lexical resources. Our approach is grounded on a list of neoclassical formative elements pertaining to the medical domain an on a large sized corpus of medical diagnoses. The outcomes of this work are automatically built electronic dictionaries and thesauri and an annotated corpus for the NLP in the medical domain.

An Advanced Methodology to Analyse Data Stored on Mobile Devices

Nowadays computer and mobile devices, such as mobile phones, smartphones, smartwatches, tablets, etc., represent the multimedia diary of each of us. Thanks to technological evolution and the advent of an infinite number of applications, mainly aimed at socialization and entertainment, they have become the containers of an infinite number of personal and professional information. For this reason, optimizing the performance of systems able to detect intrusions (IDS - Intrusion Detection System) is a goal of common interest. This paper presents a methodology to classify hacking attacks taking advantage of the generalization property of neural networks. In particular, in this work we adopt the multilayer perceptron (MLP) model with the back-propagation algorithm and the sigmoidal activation function. We analyse the results obtained using different configurations for the neural network, varying the number of hidden layers and the number of training epochs in order to obtain a low number of false positives. The obtained results will be presented in terms of type of attacks and training epochs and we will show that the best classification is carried out for DOS and Probe attacks.

Improving Results of Forensics Analysis by Semantic-Based Suggestion System

Nowadays, more than ever, digital forensics activities are involved in any criminal, civil or military investigation and they are primary to support cyber-security. Detectives use a many techniques and proprietary forensic software to analyze (copies of) digital devices, in order to discover hidden, deleted, encrypted, and damaged files or folders. Any evidence found is carefully analysed and documented in “finding reports” that are used during lawsuits. Forensics aim at discovering and analysing patterns of fraudulent activities. In this work, we propose a methodology that supports detectives in correlating evidences found by different forensic tools and we apply it to a framework able to semantically annotate data generated by forensics tools. Annotations enable more effective access to relevant information and enhanced retrieval and reasoning.

ECT: A Novel Architecture for Evidence Collection in Forensic Investigation

Computers, smart-phones, wearable devices, and more generally any piece of technology that processes information can be used in a criminal way. Forensics investigation is becoming a crucial process to acquire and secure information and data that is stored on, received or transmitted by an electronic device for further examination during a trial. In this work, we adopt semantics-based technologies to compose a methodology supporting forensics investigations during the analysis process. Proposed methodology aims to infer hidden correlations among different informative sources, for example, integrating in a unique coherent stream the results of different forensic tools. We implement this methodology through a system able to generate additional assertion to data generated by forensics tools during extraction processes. Thanks to their formal representation, assertions enable enhanced retrieval and reasoning capabilities and so more efficient access to information resources. In order to prove effectiveness of the approach, we show some experimental results obtained in a simulated case of a digital investigation examination, evaluating recall and precision of query results, asking for a set of given evidences considered as ground truth.

Semantic Integration and Correlation of Digital Evidences in Forensic Investigations

The growing adoption of digital and computer-related technologies has led to the increase of computer crimes occurrences. Today we are exposed to a great number of threats that involve IT technologies, such as terrorism or cyber-scams through Internet, child pornography, espionage and tampering of computer equipment. Digital investigations play a leading role to guarantee a legal use of digital technologies, as well to search for evidences left by the author of a crime. Digital investigations often combine a wide span of skills and require the adoption of different tools and techniques in order to capture, store and analyze evidences from a Computer Crime Scene. The lack of integration and interoperability between these tools protract the analysis process and pauperize presentation results for non-technical parties (from legal, academic or business areas). This work presents a method, based on Semantic Web technologies, to integrate, correlate and query different sources of forensic data with the goal of more valuable reconstruction of a case.

Modeling Approach for Specialist Domain.

In the last two decades data analytics applications have been studied fairly extensively in the general context of analyzing business data, interactions between people through social networks analysis, marketing trends and so on. The increasing ability to capture and handle geographic data means that spatial analysis is occurring within increasingly data-rich environments. Although GIS provide platforms for managing these data, many of this applications require the integration or the cooperation between multiple information systems in order to satisfy the users’ needs with accurate, not-ambiguous and not-redundant information. In this paper we present a methodology for modeling a domain of interest through semantic Web technologies, and we show a method to query the produced domain model in order to retrieve pertinent information.